此数据基于中国区域1km逐月平均温度数据集（1952-2019年）（https://www.ncdc.ac.cn/portal/metadata/f45a3dc7-25a8-4494-8195-dcaec5d76c92） 计算，先对东北地区进行裁剪，然后通过求平均的方式将逐月数据计算为年数据，并根据实地观测数据进行了质量控制和验证。", "ds_source": "
中国区域1km逐月平均温度数据集（1952-2019年）（https://www.ncdc.ac.cn/portal/metadata/f45a3dc7-25a8-4494-8195-dcaec5d76c92）", "ds_process_way": "
采用CAI方法生成1952—2019年中国气温表面（ChinaClim_time系列）数据，温度距平时间序列由气象站原始时间序列与30年正常值的比值和差值计算得出。结合每个气象站的经度、纬度、海拔、到最近海岸的距离、卫星驱动的距平（比率）、CRU距平（比率）和30年正常值，基于其地理坐标，应用TPS模型生成了1952.01-2019.12的温度距平面，其方法与ChinaClim_baseline类似。对于1952-2019年月距平/比值，采用不同的变量组合（经度、纬度、海拔、距最近海岸距离、CRU距平（比值）和30年正常值）构建了7个模型公式，并通过多年（1952-2019年）平均值的最小RMSE值来选择最优模型，以拟合11952-2000年温度距平面。ChinaClim_time序列是通过叠加（乘法）1952.01-2019.12的月度异常（比率）面和ChinaClim_baseline生成的。\n
然后对东北地区进行裁剪，通过求平均的方式将逐月数据计算为年数据，并根据实地观测数据进行了质量控制和验证。", "ds_quality": "
数据显著改善了降水估算精度，适用于气候变化时空格局及其生态环境响应的研究。", "ds_acq_start_time": "1952-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "东北地区", "ds_acq_lon_east": 135.08861111111113, "ds_acq_lat_south": 40.0, "ds_acq_lon_west": 109.99694444444445, "ds_acq_lat_north": 53.55833333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1333518022, "ds_files_count": 69, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "a6cdfa28-5d09-4d03-be33-0596527e63ac.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "221ebf56-1b0b-4574-972b-1fb6d3cf1be7", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2026-04-07 14:56:18", "last_updated": "2026-05-12 11:24:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7294.2026", "i18n": { "en": { "title": "1km average air temperature spatial distribution data of Northeast China (1952-2019)", "ds_format": "*.tif", "ds_source": "
Monthly average temperature dataset for 1km in China region (1952-2019)（ https://www.ncdc.ac.cn/portal/metadata/f45a3dc7-25a8-4494-8195-dcaec5d76c92 ）", "ds_quality": "
The data significantly improves the accuracy of precipitation estimation and is suitable for studying the spatiotemporal patterns of climate change and its ecological environment response.", "ds_ref_way": "", "ds_abstract": "
This data is based on the monthly average temperature dataset of 1km in China region (1952-2019)（ https://www.ncdc.ac.cn/portal/metadata/f45a3dc7-25a8-4494-8195-dcaec5d76c92 ）Calculation: Firstly, the Northeast region was cropped, and then monthly data was calculated as annual data by averaging. Quality control and verification were carried out based on field observation data.", "ds_time_res": "", "ds_acq_place": "Northeast China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The CAI method was used to generate the ChinaClim time series temperature surface data from 1952 to 2019 in China. The temperature anomaly time series was calculated by the ratio and difference between the original time series of meteorological stations and the 30-year normal values. Based on the longitude, latitude, altitude, distance to the nearest coast, satellite driven anomaly (ratio), CRU anomaly (ratio), and 30-year normal values of each meteorological station, the TPS model was applied to generate a temperature anomaly plane from January 1952 to December 2019, using a method similar to the ChinaClim baseline. For the monthly anomaly/ratio from 1952 to 2019, seven model formulas were constructed using different combinations of variables (longitude, latitude, altitude, distance to the nearest coast, CRU anomaly (ratio), and 30-year normal value), and the optimal model was selected based on the minimum RMSE value of the multi-year (1952-2019) average to fit the temperature anomaly plane from 11952-2000. The ChinaClim_time sequence is generated by superimposing (multiplying) the monthly anomaly (ratio) surface from 1952.01-209.12 and the ChinaClim_baseline.\r\n
Then, the Northeast region was cropped and the monthly data was calculated as annual data by averaging, and quality control and verification were carried out based on field observation data.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "东北地区", "1km", "平均气温" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "东北地区" ], "ds_time_tags": [ 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "李国玉", "email": "guoyuli@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈敦", "email": "chendun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "陈敦", "email": "chendun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冻土" }